SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

• Published in: Forests 2023-10, Vol.14 (10), p.1985
• Main Authors: Sun, Xinpeng, Zhou, Cui, Xie, Jian, Ouyang, Zidu, Luo, Yongfeng
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Anomalies; Artificial intelligence; Artificial neural networks; Belief networks; Comparative analysis; Coniferous forests; Deep learning; digital elevation model; Digital Elevation Models; Error analysis; Error correction; Error correction & detection; forest; Forestry; Geology; Hydrology; ICESat-2; Land area; Machine learning; Methods; Mountain regions; Mountainous areas; Mountains; Neural networks; Oceanography; Particle swarm optimization; Prediction models; PSO-DBN; Radar; Radar imaging; Regression analysis; Root-mean-square errors; Satellite imaging; Sea level; SRTM DEM; Technology application; Topography; Unmanned aerial vehicles; Vegetation; Vegetation cover
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f14101985

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear relationship between the error and influencing factors. Additionally, traditional ANN models are susceptible to overfitting, resulting in subpar accuracy. Deep Belief Network (DBN) is a highly precise algorithm in deep learning. However, the intricate combination of hyperparameters often leads to limited generalization ability and model robustness when correcting DEM. The present study proposes an error prediction model based on the DBN optimized by Particle Swarm Optimization (PSO) for SRTM DEM correction. By utilizing the PSO algorithm, we aim to identify the optimal combination of hyperparameters of DBN, including the number of neurons in the hidden layer and the learning rates. The experiment focuses on two regions in Hunan Province, China, characterized by abundant vegetation cover. The reference data utilized for comparison is ICESat/GLAS data. The experimental results demonstrate that the mean error (ME) and root mean square error (RMSE) of the SRTM DEM corrected by the proposed algorithm in these two regions are significantly reduced by 93.5%–96.0% and 21.5%–23.5%, respectively. Moreover, there is an improvement of over 26.1% in accuracy within complex terrain areas. Specifically, in broadleaf forest, the PSO-DBN method exhibits a remarkable accuracy improvement of 26.2%, while the DBN-corrected SRTM DEM shows an improvement of 15.3%. In coniferous forest, the PSO-DBN method achieves an accuracy improvement of 14.8%, whereas the DBN-corrected SRTM DEM demonstrates a gain of 5.8%. The approach provides a more effective and robust tool for correcting SRTM DEM or other similar DEMs over vegetated mountain areas.